Researchers from Harvard University and the Massachusetts Institute of Technology have developed an artificial intelligence (A.I.) neural network to better help detect earthquakes of all sizes.

In a recent study published in the journal Science Advances, the A.I.system was shown to be more accurate than current methods, and may help bring seismologists closer to the elusive goal of earthquake prediction, Digital Trends reports.

The paper’s focus is on earthquakes in Oklahoma, a previously seismically inactive state that has become increasingly more active over the past decade due in part to the wastewater disposal practices of the fracking industry. Since Oklahomans have never really had to worry about earthquakes, the state is ill-equipped to detect and locate them.

“One way we usually locate earthquakes is by using multiple stations and triangulation, just like GPS,” Thibaut Perol, an A.I. researcher at Harvard and one of the authors of the study, told Digital Trends.

“But in that region of Oklahoma, which has only been active seismically for a short amount of time, there aren’t a lot of seismic stations that would allow you to do triangulation. What we’ve done is to allow someone to locate an earthquake using only a single station.”

The trick used by Perol and his team was to increase the sensitivity of Oklahoma’s sparse seismographs, using a convolutional neural network to filter through the noise associated with the Earth’s goings-on — from human activity like traffic to the vibrations created by wind and waves.

To do this, they fed the A.I. data on regions that are seismically inactive, enabling the system to identify ambient noise that’s not the result of tremors. By being able to identify this ambient noise, the system can then better pick up on the importance stuff — i.e., earthquakes.

This research team led by deep learning scientist Thibaut Perol has found a way to use artificial intelligence (AI) to improve earthquake detection. Their study was published today in Science Advances.